Overview

Dataset statistics

Number of variables23
Number of observations12250
Missing cells7004
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory184.0 B

Variable types

Numeric2
DateTime2
Categorical4
Text15

Alerts

id is highly overall correlated with team and 3 other fieldsHigh correlation
team is highly overall correlated with idHigh correlation
nationality is highly overall correlated with idHigh correlation
position is highly overall correlated with idHigh correlation
age is highly overall correlated with idHigh correlation
nationality is highly imbalanced (59.8%)Imbalance
age is highly imbalanced (53.6%)Imbalance
to_year has 7004 (57.2%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-08-25 17:34:35.057784
Analysis finished2023-08-25 17:38:01.648992
Duration3 minutes and 26.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct12250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6125.5
Minimum1
Maximum12250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:01.771347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile613.45
Q13063.25
median6125.5
Q39187.75
95-th percentile11637.55
Maximum12250
Range12249
Interquartile range (IQR)6124.5

Descriptive statistics

Standard deviation3536.4147
Coefficient of variation (CV)0.57732671
Kurtosis-1.2
Mean6125.5
Median Absolute Deviation (MAD)3062.5
Skewness0
Sum75037375
Variance12506229
MonotonicityStrictly increasing
2023-08-25T14:38:01.925406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
8171 1
 
< 0.1%
8162 1
 
< 0.1%
8163 1
 
< 0.1%
8164 1
 
< 0.1%
8165 1
 
< 0.1%
8166 1
 
< 0.1%
8167 1
 
< 0.1%
8168 1
 
< 0.1%
8169 1
 
< 0.1%
Other values (12240) 12240
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
12250 1
< 0.1%
12249 1
< 0.1%
12248 1
< 0.1%
12247 1
< 0.1%
12246 1
< 0.1%
12245 1
< 0.1%
12244 1
< 0.1%
12243 1
< 0.1%
12242 1
< 0.1%
12241 1
< 0.1%

year
Date

Distinct60
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
Minimum1963-01-01 00:00:00
Maximum2022-01-01 00:00:00
2023-08-25T14:38:02.072486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-25T14:38:02.224737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

overall_pick
Real number (ℝ)

Distinct293
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.63314
Minimum1
Maximum293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:02.375827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q155
median112
Q3174
95-th percentile238
Maximum293
Range292
Interquartile range (IQR)119

Descriptive statistics

Standard deviation72.030642
Coefficient of variation (CV)0.61758296
Kurtosis-0.95823994
Mean116.63314
Median Absolute Deviation (MAD)59.5
Skewness0.22542791
Sum1428756
Variance5188.4134
MonotonicityNot monotonic
2023-08-25T14:38:02.527355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 60
 
0.5%
6 60
 
0.5%
9 60
 
0.5%
8 60
 
0.5%
7 60
 
0.5%
10 60
 
0.5%
5 60
 
0.5%
4 60
 
0.5%
3 60
 
0.5%
2 60
 
0.5%
Other values (283) 11650
95.1%
ValueCountFrequency (%)
1 60
0.5%
2 60
0.5%
3 60
0.5%
4 60
0.5%
5 60
0.5%
6 60
0.5%
7 60
0.5%
8 60
0.5%
9 60
0.5%
10 60
0.5%
ValueCountFrequency (%)
293 1
 
< 0.1%
292 2
 
< 0.1%
291 4
< 0.1%
290 4
< 0.1%
289 5
< 0.1%
288 5
< 0.1%
287 5
< 0.1%
286 7
0.1%
285 7
0.1%
284 7
0.1%

team
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
Montreal Canadiens
 
627
New York Rangers
 
551
Chicago Blackhawks
 
545
Detroit Red Wings
 
534
Philadelphia Flyers
 
509
Other values (39)
9484 

Length

Max length23
Median length20
Mean length16.696408
Min length0

Characters and Unicode

Total characters204531
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMontreal Canadiens
2nd rowNew Jersey Devils
3rd rowArizona Coyotes
4th rowSeattle Kraken
5th rowPhiladelphia Flyers

Common Values

ValueCountFrequency (%)
Montreal Canadiens 627
 
5.1%
New York Rangers 551
 
4.5%
Chicago Blackhawks 545
 
4.4%
Detroit Red Wings 534
 
4.4%
Philadelphia Flyers 509
 
4.2%
Toronto Maple Leafs 509
 
4.2%
St. Louis Blues 498
 
4.1%
New York Islanders 496
 
4.0%
Buffalo Sabres 496
 
4.0%
Los Angeles Kings 475
 
3.9%
Other values (34) 7010
57.2%

Length

2023-08-25T14:38:02.665986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 1433
 
4.9%
york 1047
 
3.6%
montreal 627
 
2.2%
canadiens 627
 
2.2%
rangers 551
 
1.9%
chicago 545
 
1.9%
blackhawks 545
 
1.9%
detroit 534
 
1.8%
red 534
 
1.8%
wings 534
 
1.8%
Other values (79) 22065
76.0%

Most occurring characters

ValueCountFrequency (%)
a 18512
 
9.1%
s 16814
 
8.2%
16793
 
8.2%
e 16409
 
8.0%
n 13650
 
6.7%
o 12484
 
6.1%
r 11479
 
5.6%
i 10717
 
5.2%
l 10283
 
5.0%
t 9436
 
4.6%
Other values (40) 67954
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 158198
77.3%
Uppercase Letter 29042
 
14.2%
Space Separator 16793
 
8.2%
Other Punctuation 498
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18512
11.7%
s 16814
10.6%
e 16409
10.4%
n 13650
8.6%
o 12484
 
7.9%
r 11479
 
7.3%
i 10717
 
6.8%
l 10283
 
6.5%
t 9436
 
6.0%
h 5299
 
3.3%
Other values (15) 33115
20.9%
Uppercase Letter
ValueCountFrequency (%)
C 3431
 
11.8%
B 2948
 
10.2%
S 2372
 
8.2%
N 2081
 
7.2%
P 2032
 
7.0%
L 1751
 
6.0%
W 1610
 
5.5%
M 1562
 
5.4%
D 1378
 
4.7%
F 1236
 
4.3%
Other values (13) 8641
29.8%
Space Separator
ValueCountFrequency (%)
16793
100.0%
Other Punctuation
ValueCountFrequency (%)
. 498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 187240
91.5%
Common 17291
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18512
 
9.9%
s 16814
 
9.0%
e 16409
 
8.8%
n 13650
 
7.3%
o 12484
 
6.7%
r 11479
 
6.1%
i 10717
 
5.7%
l 10283
 
5.5%
t 9436
 
5.0%
h 5299
 
2.8%
Other values (38) 62157
33.2%
Common
ValueCountFrequency (%)
16793
97.1%
. 498
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18512
 
9.1%
s 16814
 
8.2%
16793
 
8.2%
e 16409
 
8.0%
n 13650
 
6.7%
o 12484
 
6.1%
r 11479
 
5.6%
i 10717
 
5.2%
l 10283
 
5.0%
t 9436
 
4.6%
Other values (40) 67954
33.2%

player
Text

Distinct12030
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:02.866251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length12.865714
Min length7

Characters and Unicode

Total characters157605
Distinct characters63
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11823 ?
Unique (%)96.5%

Sample

1st rowJuraj Slafkovsky
2nd rowSimon Nemec
3rd rowLogan Cooley
4th rowShane Wright
5th rowCutter Gauthier
ValueCountFrequency (%)
mike 270
 
1.1%
john 187
 
0.8%
ryan 162
 
0.7%
brian 145
 
0.6%
david 144
 
0.6%
chris 143
 
0.6%
steve 134
 
0.5%
jeff 134
 
0.5%
mark 129
 
0.5%
dave 128
 
0.5%
Other values (9365) 23014
93.6%
2023-08-25T14:38:03.207131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14194
 
9.0%
a 13103
 
8.3%
12340
 
7.8%
n 11080
 
7.0%
r 10749
 
6.8%
i 9936
 
6.3%
o 9307
 
5.9%
l 7263
 
4.6%
t 5906
 
3.7%
s 5868
 
3.7%
Other values (53) 57859
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 119774
76.0%
Uppercase Letter 25190
 
16.0%
Space Separator 12340
 
7.8%
Other Punctuation 187
 
0.1%
Dash Punctuation 110
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Modifier Letter 1
 
< 0.1%
Other Number 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 2603
 
10.3%
B 2089
 
8.3%
J 2036
 
8.1%
S 2017
 
8.0%
D 1753
 
7.0%
C 1521
 
6.0%
R 1441
 
5.7%
P 1311
 
5.2%
K 1246
 
4.9%
A 1199
 
4.8%
Other values (18) 7974
31.7%
Lowercase Letter
ValueCountFrequency (%)
e 14194
11.9%
a 13103
10.9%
n 11080
 
9.3%
r 10749
 
9.0%
i 9936
 
8.3%
o 9307
 
7.8%
l 7263
 
6.1%
t 5906
 
4.9%
s 5868
 
4.9%
u 3763
 
3.1%
Other values (16) 28605
23.9%
Other Punctuation
ValueCountFrequency (%)
. 105
56.1%
' 80
42.8%
¶ 2
 
1.1%
Space Separator
ValueCountFrequency (%)
12340
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Modifier Letter
ValueCountFrequency (%)
ˆ 1
100.0%
Other Number
ValueCountFrequency (%)
¼ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 144964
92.0%
Common 12641
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14194
 
9.8%
a 13103
 
9.0%
n 11080
 
7.6%
r 10749
 
7.4%
i 9936
 
6.9%
o 9307
 
6.4%
l 7263
 
5.0%
t 5906
 
4.1%
s 5868
 
4.0%
u 3763
 
2.6%
Other values (44) 53795
37.1%
Common
ValueCountFrequency (%)
12340
97.6%
- 110
 
0.9%
. 105
 
0.8%
' 80
 
0.6%
¶ 2
 
< 0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%
ˆ 1
 
< 0.1%
¼ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157597
> 99.9%
None 7
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14194
 
9.0%
a 13103
 
8.3%
12340
 
7.8%
n 11080
 
7.0%
r 10749
 
6.8%
i 9936
 
6.3%
o 9307
 
5.9%
l 7263
 
4.6%
t 5906
 
3.7%
s 5868
 
3.7%
Other values (48) 57851
36.7%
None
ValueCountFrequency (%)
à 3
42.9%
¶ 2
28.6%
Ã… 1
 
14.3%
¼ 1
 
14.3%
Modifier Letters
ValueCountFrequency (%)
ˆ 1
100.0%

nationality
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct47
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
CA
6498 
US
2639 
SE
800 
RU
724 
FI
 
497
Other values (42)
1092 

Length

Max length2
Median length2
Mean length1.9993469
Min length0

Characters and Unicode

Total characters24492
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st rowSK
2nd rowSK
3rd rowUS
4th rowCA
5th rowSE

Common Values

ValueCountFrequency (%)
CA 6498
53.0%
US 2639
21.5%
SE 800
 
6.5%
RU 724
 
5.9%
FI 497
 
4.1%
CZ 479
 
3.9%
SK 166
 
1.4%
DE 81
 
0.7%
CH 73
 
0.6%
LV 39
 
0.3%
Other values (37) 254
 
2.1%

Length

2023-08-25T14:38:03.359723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 6498
53.1%
us 2639
21.5%
se 800
 
6.5%
ru 724
 
5.9%
fi 497
 
4.1%
cz 479
 
3.9%
sk 166
 
1.4%
de 81
 
0.7%
ch 73
 
0.6%
lv 39
 
0.3%
Other values (36) 250
 
2.0%

Most occurring characters

ValueCountFrequency (%)
C 7051
28.8%
A 6552
26.8%
S 3618
14.8%
U 3408
13.9%
E 889
 
3.6%
R 737
 
3.0%
I 508
 
2.1%
Z 508
 
2.1%
F 506
 
2.1%
K 219
 
0.9%
Other values (14) 496
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24492
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 7051
28.8%
A 6552
26.8%
S 3618
14.8%
U 3408
13.9%
E 889
 
3.6%
R 737
 
3.0%
I 508
 
2.1%
Z 508
 
2.1%
F 506
 
2.1%
K 219
 
0.9%
Other values (14) 496
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24492
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 7051
28.8%
A 6552
26.8%
S 3618
14.8%
U 3408
13.9%
E 889
 
3.6%
R 737
 
3.0%
I 508
 
2.1%
Z 508
 
2.1%
F 506
 
2.1%
K 219
 
0.9%
Other values (14) 496
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 7051
28.8%
A 6552
26.8%
S 3618
14.8%
U 3408
13.9%
E 889
 
3.6%
R 737
 
3.0%
I 508
 
2.1%
Z 508
 
2.1%
F 506
 
2.1%
K 219
 
0.9%
Other values (14) 496
 
2.0%

position
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
D
3966 
C
2688 
LW
2080 
RW
2021 
G
1217 
Other values (20)
 
278

Length

Max length5
Median length1
Mean length1.3783673
Min length0

Characters and Unicode

Total characters16885
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowLW
2nd rowD
3rd rowC
4th rowC
5th rowLW

Common Values

ValueCountFrequency (%)
D 3966
32.4%
C 2688
21.9%
LW 2080
17.0%
RW 2021
16.5%
G 1217
 
9.9%
C/LW 74
 
0.6%
C/RW 49
 
0.4%
W 44
 
0.4%
27
 
0.2%
LW/C 18
 
0.1%
Other values (15) 66
 
0.5%

Length

2023-08-25T14:38:03.491655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d 3966
32.4%
c 2694
22.0%
lw 2082
17.0%
rw 2023
16.5%
g 1217
 
10.0%
c/lw 74
 
0.6%
c/rw 49
 
0.4%
w 44
 
0.4%
lw/c 18
 
0.1%
f 18
 
0.1%
Other values (13) 46
 
0.4%

Most occurring characters

ValueCountFrequency (%)
W 4324
25.6%
D 3995
23.7%
C 2854
16.9%
L 2189
13.0%
R 2090
12.4%
G 1217
 
7.2%
/ 184
 
1.1%
F 18
 
0.1%
8
 
< 0.1%
; 2
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16687
98.8%
Other Punctuation 186
 
1.1%
Space Separator 8
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 4324
25.9%
D 3995
23.9%
C 2854
17.1%
L 2189
13.1%
R 2090
12.5%
G 1217
 
7.3%
F 18
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
25.0%
n 1
25.0%
t 1
25.0%
r 1
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 184
98.9%
; 2
 
1.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16691
98.9%
Common 194
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 4324
25.9%
D 3995
23.9%
C 2854
17.1%
L 2189
13.1%
R 2090
12.5%
G 1217
 
7.3%
F 18
 
0.1%
e 1
 
< 0.1%
n 1
 
< 0.1%
t 1
 
< 0.1%
Common
ValueCountFrequency (%)
/ 184
94.8%
8
 
4.1%
; 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 4324
25.6%
D 3995
23.7%
C 2854
16.9%
L 2189
13.0%
R 2090
12.4%
G 1217
 
7.2%
/ 184
 
1.1%
F 18
 
0.1%
8
 
< 0.1%
; 2
 
< 0.1%
Other values (4) 4
 
< 0.1%

age
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
18
5231 
3959 
19
1493 
20
1268 
21
 
70
Other values (14)
 
229

Length

Max length2
Median length2
Mean length1.3536327
Min length0

Characters and Unicode

Total characters16582
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row18
2nd row18
3rd row18
4th row18
5th row18

Common Values

ValueCountFrequency (%)
18 5231
42.7%
3959
32.3%
19 1493
 
12.2%
20 1268
 
10.4%
21 70
 
0.6%
22 36
 
0.3%
25 32
 
0.3%
23 30
 
0.2%
24 26
 
0.2%
27 24
 
0.2%
Other values (9) 81
 
0.7%

Length

2023-08-25T14:38:03.619011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18 5231
63.1%
19 1493
 
18.0%
20 1268
 
15.3%
21 70
 
0.8%
22 36
 
0.4%
25 32
 
0.4%
23 30
 
0.4%
24 26
 
0.3%
27 24
 
0.3%
17 22
 
0.3%
Other values (8) 59
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 6827
41.2%
8 5242
31.6%
2 1561
 
9.4%
9 1499
 
9.0%
0 1276
 
7.7%
7 47
 
0.3%
3 43
 
0.3%
5 32
 
0.2%
6 29
 
0.2%
4 26
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16582
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6827
41.2%
8 5242
31.6%
2 1561
 
9.4%
9 1499
 
9.0%
0 1276
 
7.7%
7 47
 
0.3%
3 43
 
0.3%
5 32
 
0.2%
6 29
 
0.2%
4 26
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 16582
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6827
41.2%
8 5242
31.6%
2 1561
 
9.4%
9 1499
 
9.0%
0 1276
 
7.7%
7 47
 
0.3%
3 43
 
0.3%
5 32
 
0.2%
6 29
 
0.2%
4 26
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6827
41.2%
8 5242
31.6%
2 1561
 
9.4%
9 1499
 
9.0%
0 1276
 
7.7%
7 47
 
0.3%
3 43
 
0.3%
5 32
 
0.2%
6 29
 
0.2%
4 26
 
0.2%

to_year
Date

MISSING 

Distinct54
Distinct (%)1.0%
Missing7004
Missing (%)57.2%
Memory size95.8 KiB
Minimum1968-01-01 00:00:00
Maximum2022-01-01 00:00:00
2023-08-25T14:38:03.755188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-25T14:38:03.900952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1548
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:04.126305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length38
Mean length23.436571
Min length2

Characters and Unicode

Total characters287098
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique639 ?
Unique (%)5.2%

Sample

1st rowTPS (Finland)
2nd rowHK Nitra (Slovakia)
3rd rowUSA U-18 Development Team (USDP/USHL)
4th rowKingston Frontenacs (OHL)
5th rowUSA U-18 Development Team (USDP/USHL)
ValueCountFrequency (%)
ohl 1852
 
4.6%
whl 1721
 
4.3%
jr 1594
 
4.0%
qmjhl 1240
 
3.1%
sweden 676
 
1.7%
ushl 455
 
1.1%
finland 450
 
1.1%
hs 445
 
1.1%
hc 440
 
1.1%
wchl 413
 
1.0%
Other values (1516) 30805
76.8%
2023-08-25T14:38:04.506120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27841
 
9.7%
e 17755
 
6.2%
a 16489
 
5.7%
r 13696
 
4.8%
n 12834
 
4.5%
o 12487
 
4.3%
i 12443
 
4.3%
( 12275
 
4.3%
) 12275
 
4.3%
s 11909
 
4.1%
Other values (60) 137094
47.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 157229
54.8%
Uppercase Letter 70411
24.5%
Space Separator 27841
 
9.7%
Open Punctuation 12275
 
4.3%
Close Punctuation 12275
 
4.3%
Other Punctuation 2937
 
1.0%
Dash Punctuation 2614
 
0.9%
Decimal Number 1516
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17755
11.3%
a 16489
10.5%
r 13696
 
8.7%
n 12834
 
8.2%
o 12487
 
7.9%
i 12443
 
7.9%
s 11909
 
7.6%
t 9749
 
6.2%
l 8535
 
5.4%
h 5243
 
3.3%
Other values (16) 36089
23.0%
Uppercase Letter
ValueCountFrequency (%)
H 11436
16.2%
L 8012
11.4%
S 6145
 
8.7%
C 5372
 
7.6%
J 4511
 
6.4%
M 4253
 
6.0%
W 3726
 
5.3%
O 3567
 
5.1%
A 3257
 
4.6%
B 2297
 
3.3%
Other values (16) 17835
25.3%
Decimal Number
ValueCountFrequency (%)
2 467
30.8%
1 288
19.0%
8 229
15.1%
7 209
13.8%
6 175
 
11.5%
3 117
 
7.7%
9 14
 
0.9%
4 11
 
0.7%
0 5
 
0.3%
5 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 2349
80.0%
' 349
 
11.9%
/ 223
 
7.6%
& 16
 
0.5%
Space Separator
ValueCountFrequency (%)
27841
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12275
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12275
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2614
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 227640
79.3%
Common 59458
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17755
 
7.8%
a 16489
 
7.2%
r 13696
 
6.0%
n 12834
 
5.6%
o 12487
 
5.5%
i 12443
 
5.5%
s 11909
 
5.2%
H 11436
 
5.0%
t 9749
 
4.3%
l 8535
 
3.7%
Other values (42) 100307
44.1%
Common
ValueCountFrequency (%)
27841
46.8%
( 12275
20.6%
) 12275
20.6%
- 2614
 
4.4%
. 2349
 
4.0%
2 467
 
0.8%
' 349
 
0.6%
1 288
 
0.5%
8 229
 
0.4%
/ 223
 
0.4%
Other values (8) 548
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27841
 
9.7%
e 17755
 
6.2%
a 16489
 
5.7%
r 13696
 
4.8%
n 12834
 
4.5%
o 12487
 
4.3%
i 12443
 
4.3%
( 12275
 
4.3%
) 12275
 
4.3%
s 11909
 
4.1%
Other values (60) 137094
47.8%
Distinct1115
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:04.751692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length0
Mean length1.0562449
Min length0

Characters and Unicode

Total characters12939
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique300 ?
Unique (%)2.4%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
1 199
 
3.8%
2 151
 
2.9%
3 116
 
2.2%
4 84
 
1.6%
6 61
 
1.2%
5 60
 
1.1%
9 57
 
1.1%
8 56
 
1.1%
7 56
 
1.1%
10 48
 
0.9%
Other values (1104) 4358
83.1%
2023-08-25T14:38:05.143822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2302
17.8%
2 1608
12.4%
3 1375
10.6%
4 1257
9.7%
5 1223
9.5%
6 1153
8.9%
7 1112
8.6%
8 991
7.7%
9 977
7.6%
0 941
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12939
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2302
17.8%
2 1608
12.4%
3 1375
10.6%
4 1257
9.7%
5 1223
9.5%
6 1153
8.9%
7 1112
8.6%
8 991
7.7%
9 977
7.6%
0 941
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 12939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2302
17.8%
2 1608
12.4%
3 1375
10.6%
4 1257
9.7%
5 1223
9.5%
6 1153
8.9%
7 1112
8.6%
8 991
7.7%
9 977
7.6%
0 941
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2302
17.8%
2 1608
12.4%
3 1375
10.6%
4 1257
9.7%
5 1223
9.5%
6 1153
8.9%
7 1112
8.6%
8 991
7.7%
9 977
7.6%
0 941
7.3%

goals
Text

Distinct411
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:05.381188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length0
Mean length0.70979592
Min length0

Characters and Unicode

Total characters8695
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)1.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 1365
26.0%
1 349
 
6.7%
2 228
 
4.3%
3 161
 
3.1%
4 143
 
2.7%
5 97
 
1.8%
7 88
 
1.7%
6 87
 
1.7%
8 77
 
1.5%
9 67
 
1.3%
Other values (400) 2584
49.3%
2023-08-25T14:38:05.753937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1763
20.3%
1 1731
19.9%
2 1114
12.8%
3 790
9.1%
4 735
8.5%
5 590
 
6.8%
6 542
 
6.2%
7 501
 
5.8%
8 482
 
5.5%
9 447
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8695
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1763
20.3%
1 1731
19.9%
2 1114
12.8%
3 790
9.1%
4 735
8.5%
5 590
 
6.8%
6 542
 
6.2%
7 501
 
5.8%
8 482
 
5.5%
9 447
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1763
20.3%
1 1731
19.9%
2 1114
12.8%
3 790
9.1%
4 735
8.5%
5 590
 
6.8%
6 542
 
6.2%
7 501
 
5.8%
8 482
 
5.5%
9 447
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1763
20.3%
1 1731
19.9%
2 1114
12.8%
3 790
9.1%
4 735
8.5%
5 590
 
6.8%
6 542
 
6.2%
7 501
 
5.8%
8 482
 
5.5%
9 447
 
5.1%
Distinct569
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:05.992897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length0
Mean length0.79273469
Min length0

Characters and Unicode

Total characters9711
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)1.5%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 847
 
16.1%
1 320
 
6.1%
2 237
 
4.5%
3 165
 
3.1%
4 142
 
2.7%
5 126
 
2.4%
6 95
 
1.8%
8 85
 
1.6%
7 79
 
1.5%
10 71
 
1.4%
Other values (558) 3079
58.7%
2023-08-25T14:38:06.377769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1969
20.3%
0 1361
14.0%
2 1331
13.7%
3 1044
10.8%
4 856
8.8%
5 735
 
7.6%
6 673
 
6.9%
7 624
 
6.4%
8 565
 
5.8%
9 553
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9711
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1969
20.3%
0 1361
14.0%
2 1331
13.7%
3 1044
10.8%
4 856
8.8%
5 735
 
7.6%
6 673
 
6.9%
7 624
 
6.4%
8 565
 
5.8%
9 553
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1969
20.3%
0 1361
14.0%
2 1331
13.7%
3 1044
10.8%
4 856
8.8%
5 735
 
7.6%
6 673
 
6.9%
7 624
 
6.4%
8 565
 
5.8%
9 553
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1969
20.3%
0 1361
14.0%
2 1331
13.7%
3 1044
10.8%
4 856
8.8%
5 735
 
7.6%
6 673
 
6.9%
7 624
 
6.4%
8 565
 
5.8%
9 553
 
5.7%

points
Text

Distinct772
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:06.618227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length0
Mean length0.84563265
Min length0

Characters and Unicode

Total characters10359
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)2.4%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 732
 
14.0%
1 307
 
5.9%
2 191
 
3.6%
3 153
 
2.9%
4 128
 
2.4%
6 103
 
2.0%
5 99
 
1.9%
7 82
 
1.6%
9 76
 
1.4%
8 67
 
1.3%
Other values (761) 3308
63.1%
2023-08-25T14:38:07.004771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1938
18.7%
2 1383
13.4%
0 1299
12.5%
3 1137
11.0%
4 954
9.2%
5 827
8.0%
6 803
7.8%
8 708
 
6.8%
7 694
 
6.7%
9 616
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10359
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1938
18.7%
2 1383
13.4%
0 1299
12.5%
3 1137
11.0%
4 954
9.2%
5 827
8.0%
6 803
7.8%
8 708
 
6.8%
7 694
 
6.7%
9 616
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 10359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1938
18.7%
2 1383
13.4%
0 1299
12.5%
3 1137
11.0%
4 954
9.2%
5 827
8.0%
6 803
7.8%
8 708
 
6.8%
7 694
 
6.7%
9 616
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1938
18.7%
2 1383
13.4%
0 1299
12.5%
3 1137
11.0%
4 954
9.2%
5 827
8.0%
6 803
7.8%
8 708
 
6.8%
7 694
 
6.7%
9 616
 
5.9%
Distinct365
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:07.200248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length0
Mean length0.90791837
Min length0

Characters and Unicode

Total characters11122
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)0.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 810
 
15.5%
1 417
 
8.0%
2 315
 
6.0%
3 230
 
4.4%
5 202
 
3.9%
4 196
 
3.7%
6 138
 
2.6%
9 122
 
2.3%
7 120
 
2.3%
8 119
 
2.3%
Other values (216) 2565
49.0%
2023-08-25T14:38:07.534670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3040
27.3%
1 1685
15.2%
0 1183
 
10.6%
2 1139
 
10.2%
3 860
 
7.7%
4 743
 
6.7%
5 634
 
5.7%
6 540
 
4.9%
7 499
 
4.5%
8 412
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8082
72.7%
Dash Punctuation 3040
 
27.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1685
20.8%
0 1183
14.6%
2 1139
14.1%
3 860
10.6%
4 743
9.2%
5 634
 
7.8%
6 540
 
6.7%
7 499
 
6.2%
8 412
 
5.1%
9 387
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 3040
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11122
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3040
27.3%
1 1685
15.2%
0 1183
 
10.6%
2 1139
 
10.2%
3 860
 
7.7%
4 743
 
6.7%
5 634
 
5.7%
6 540
 
4.9%
7 499
 
4.5%
8 412
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3040
27.3%
1 1685
15.2%
0 1183
 
10.6%
2 1139
 
10.2%
3 860
 
7.7%
4 743
 
6.7%
5 634
 
5.7%
6 540
 
4.9%
7 499
 
4.5%
8 412
 
3.7%
Distinct1080
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:07.771167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length0
Mean length0.95771429
Min length0

Characters and Unicode

Total characters11732
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique476 ?
Unique (%)3.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 618
 
11.8%
2 304
 
5.8%
4 152
 
2.9%
6 136
 
2.6%
10 79
 
1.5%
12 78
 
1.5%
8 77
 
1.5%
16 61
 
1.2%
14 52
 
1.0%
18 52
 
1.0%
Other values (1069) 3637
69.3%
2023-08-25T14:38:08.153439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1898
16.2%
2 1757
15.0%
0 1441
12.3%
4 1237
10.5%
3 1096
9.3%
6 1071
9.1%
5 906
7.7%
8 866
7.4%
7 782
6.7%
9 678
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11732
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1898
16.2%
2 1757
15.0%
0 1441
12.3%
4 1237
10.5%
3 1096
9.3%
6 1071
9.1%
5 906
7.7%
8 866
7.4%
7 782
6.7%
9 678
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 11732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1898
16.2%
2 1757
15.0%
0 1441
12.3%
4 1237
10.5%
3 1096
9.3%
6 1071
9.1%
5 906
7.7%
8 866
7.4%
7 782
6.7%
9 678
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1898
16.2%
2 1757
15.0%
0 1441
12.3%
4 1237
10.5%
3 1096
9.3%
6 1071
9.1%
5 906
7.7%
8 866
7.4%
7 782
6.7%
9 678
 
5.8%
Distinct266
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:08.400164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length0
Mean length0.087346939
Min length0

Characters and Unicode

Total characters1070
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)1.6%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
1 47
 
9.6%
2 27
 
5.5%
3 15
 
3.1%
4 15
 
3.1%
6 14
 
2.9%
11 7
 
1.4%
7 6
 
1.2%
8 5
 
1.0%
10 4
 
0.8%
30 4
 
0.8%
Other values (255) 346
70.6%
2023-08-25T14:38:08.789018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 201
18.8%
2 158
14.8%
3 114
10.7%
6 112
10.5%
4 107
10.0%
7 88
8.2%
5 83
7.8%
8 76
 
7.1%
9 69
 
6.4%
0 62
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1070
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 201
18.8%
2 158
14.8%
3 114
10.7%
6 112
10.5%
4 107
10.0%
7 88
8.2%
5 83
7.8%
8 76
 
7.1%
9 69
 
6.4%
0 62
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 201
18.8%
2 158
14.8%
3 114
10.7%
6 112
10.5%
4 107
10.0%
7 88
8.2%
5 83
7.8%
8 76
 
7.1%
9 69
 
6.4%
0 62
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 201
18.8%
2 158
14.8%
3 114
10.7%
6 112
10.5%
4 107
10.0%
7 88
8.2%
5 83
7.8%
8 76
 
7.1%
9 69
 
6.4%
0 62
 
5.8%
Distinct190
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:09.025358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length0
Mean length0.075102041
Min length0

Characters and Unicode

Total characters920
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)0.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 87
 
17.8%
1 34
 
7.0%
2 17
 
3.5%
3 16
 
3.3%
4 11
 
2.3%
7 10
 
2.0%
6 6
 
1.2%
9 5
 
1.0%
23 5
 
1.0%
80 5
 
1.0%
Other values (179) 292
59.8%
2023-08-25T14:38:09.391376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 192
20.9%
0 144
15.7%
2 131
14.2%
3 100
10.9%
6 66
 
7.2%
4 65
 
7.1%
9 61
 
6.6%
5 61
 
6.6%
7 51
 
5.5%
8 49
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 920
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 192
20.9%
0 144
15.7%
2 131
14.2%
3 100
10.9%
6 66
 
7.2%
4 65
 
7.1%
9 61
 
6.6%
5 61
 
6.6%
7 51
 
5.5%
8 49
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 192
20.9%
0 144
15.7%
2 131
14.2%
3 100
10.9%
6 66
 
7.2%
4 65
 
7.1%
9 61
 
6.6%
5 61
 
6.6%
7 51
 
5.5%
8 49
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 192
20.9%
0 144
15.7%
2 131
14.2%
3 100
10.9%
6 66
 
7.2%
4 65
 
7.1%
9 61
 
6.6%
5 61
 
6.6%
7 51
 
5.5%
8 49
 
5.3%
Distinct188
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:09.626746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length0
Mean length0.076653061
Min length0

Characters and Unicode

Total characters939
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)0.8%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 57
 
11.7%
1 35
 
7.2%
2 24
 
4.9%
4 16
 
3.3%
6 10
 
2.0%
3 8
 
1.6%
12 7
 
1.4%
10 7
 
1.4%
17 6
 
1.2%
7 6
 
1.2%
Other values (177) 312
63.9%
2023-08-25T14:38:09.989788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 200
21.3%
2 154
16.4%
3 98
10.4%
0 97
10.3%
4 88
9.4%
6 79
 
8.4%
5 62
 
6.6%
7 60
 
6.4%
9 56
 
6.0%
8 45
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 939
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 200
21.3%
2 154
16.4%
3 98
10.4%
0 97
10.3%
4 88
9.4%
6 79
 
8.4%
5 62
 
6.6%
7 60
 
6.4%
9 56
 
6.0%
8 45
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 200
21.3%
2 154
16.4%
3 98
10.4%
0 97
10.3%
4 88
9.4%
6 79
 
8.4%
5 62
 
6.6%
7 60
 
6.4%
9 56
 
6.0%
8 45
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 200
21.3%
2 154
16.4%
3 98
10.4%
0 97
10.3%
4 88
9.4%
6 79
 
8.4%
5 62
 
6.6%
7 60
 
6.4%
9 56
 
6.0%
8 45
 
4.8%
Distinct90
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:10.152602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length0
Mean length0.059673469
Min length0

Characters and Unicode

Total characters731
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.2%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 120
24.6%
1 44
 
9.0%
3 17
 
3.5%
4 16
 
3.3%
2 15
 
3.1%
9 14
 
2.9%
5 9
 
1.8%
7 9
 
1.8%
10 9
 
1.8%
14 8
 
1.6%
Other values (79) 227
46.5%
2023-08-25T14:38:10.592225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 155
21.2%
0 149
20.4%
3 75
10.3%
2 69
9.4%
4 65
8.9%
5 56
 
7.7%
7 45
 
6.2%
6 43
 
5.9%
8 40
 
5.5%
9 34
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155
21.2%
0 149
20.4%
3 75
10.3%
2 69
9.4%
4 65
8.9%
5 56
 
7.7%
7 45
 
6.2%
6 43
 
5.9%
8 40
 
5.5%
9 34
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155
21.2%
0 149
20.4%
3 75
10.3%
2 69
9.4%
4 65
8.9%
5 56
 
7.7%
7 45
 
6.2%
6 43
 
5.9%
8 40
 
5.5%
9 34
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155
21.2%
0 149
20.4%
3 75
10.3%
2 69
9.4%
4 65
8.9%
5 56
 
7.7%
7 45
 
6.2%
6 43
 
5.9%
8 40
 
5.5%
9 34
 
4.7%
Distinct117
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:10.794007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length0
Mean length0.18857143
Min length0

Characters and Unicode

Total characters2310
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)0.4%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0.912 17
 
3.5%
1 16
 
3.3%
0.902 14
 
2.9%
0.905 13
 
2.7%
0.908 12
 
2.5%
0.879 12
 
2.5%
0.906 11
 
2.3%
0.907 11
 
2.3%
0.901 11
 
2.3%
0.899 10
 
2.0%
Other values (106) 361
74.0%
2023-08-25T14:38:11.123497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 573
24.8%
. 472
20.4%
8 368
15.9%
9 318
13.8%
7 133
 
5.8%
1 123
 
5.3%
2 79
 
3.4%
5 79
 
3.4%
6 66
 
2.9%
4 50
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1838
79.6%
Other Punctuation 472
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 573
31.2%
8 368
20.0%
9 318
17.3%
7 133
 
7.2%
1 123
 
6.7%
2 79
 
4.3%
5 79
 
4.3%
6 66
 
3.6%
4 50
 
2.7%
3 49
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 573
24.8%
. 472
20.4%
8 368
15.9%
9 318
13.8%
7 133
 
5.8%
1 123
 
5.3%
2 79
 
3.4%
5 79
 
3.4%
6 66
 
2.9%
4 50
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 573
24.8%
. 472
20.4%
8 368
15.9%
9 318
13.8%
7 133
 
5.8%
1 123
 
5.3%
2 79
 
3.4%
5 79
 
3.4%
6 66
 
2.9%
4 50
 
2.2%
Distinct229
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:11.380810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length0
Mean length0.14873469
Min length0

Characters and Unicode

Total characters1822
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)0.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 18
 
3.7%
2.89 8
 
1.6%
3 7
 
1.4%
2.85 5
 
1.0%
2.71 5
 
1.0%
2.79 5
 
1.0%
2.98 5
 
1.0%
2.64 5
 
1.0%
2.7 5
 
1.0%
3.74 5
 
1.0%
Other values (218) 422
86.1%
2023-08-25T14:38:11.777865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 458
25.1%
3 284
15.6%
2 260
14.3%
4 154
 
8.5%
5 113
 
6.2%
6 109
 
6.0%
7 104
 
5.7%
8 100
 
5.5%
9 92
 
5.0%
1 86
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1364
74.9%
Other Punctuation 458
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 284
20.8%
2 260
19.1%
4 154
11.3%
5 113
 
8.3%
6 109
 
8.0%
7 104
 
7.6%
8 100
 
7.3%
9 92
 
6.7%
1 86
 
6.3%
0 62
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1822
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 458
25.1%
3 284
15.6%
2 260
14.3%
4 154
 
8.5%
5 113
 
6.2%
6 109
 
6.0%
7 104
 
5.7%
8 100
 
5.5%
9 92
 
5.0%
1 86
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 458
25.1%
3 284
15.6%
2 260
14.3%
4 154
 
8.5%
5 113
 
6.2%
6 109
 
6.0%
7 104
 
5.7%
8 100
 
5.5%
9 92
 
5.0%
1 86
 
4.7%
Distinct928
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size95.8 KiB
2023-08-25T14:38:12.060798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length0
Mean length1.4019592
Min length0

Characters and Unicode

Total characters17174
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique345 ?
Unique (%)2.8%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0.1 491
 
9.4%
0 358
 
6.8%
0.2 280
 
5.3%
0.3 196
 
3.7%
0.4 123
 
2.3%
0.5 117
 
2.2%
0.7 93
 
1.8%
0.6 86
 
1.6%
0.8 85
 
1.6%
0.9 62
 
1.2%
Other values (871) 3355
64.0%
2023-08-25T14:38:12.443252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 4501
26.2%
1 2197
12.8%
0 2141
12.5%
2 1446
 
8.4%
3 1169
 
6.8%
4 1043
 
6.1%
5 913
 
5.3%
6 821
 
4.8%
- 806
 
4.7%
7 761
 
4.4%
Other values (2) 1376
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11867
69.1%
Other Punctuation 4501
 
26.2%
Dash Punctuation 806
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2197
18.5%
0 2141
18.0%
2 1446
12.2%
3 1169
9.9%
4 1043
8.8%
5 913
7.7%
6 821
 
6.9%
7 761
 
6.4%
8 728
 
6.1%
9 648
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 4501
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 806
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4501
26.2%
1 2197
12.8%
0 2141
12.5%
2 1446
 
8.4%
3 1169
 
6.8%
4 1043
 
6.1%
5 913
 
5.3%
6 821
 
4.8%
- 806
 
4.7%
7 761
 
4.4%
Other values (2) 1376
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4501
26.2%
1 2197
12.8%
0 2141
12.5%
2 1446
 
8.4%
3 1169
 
6.8%
4 1043
 
6.1%
5 913
 
5.3%
6 821
 
4.8%
- 806
 
4.7%
7 761
 
4.4%
Other values (2) 1376
 
8.0%

Interactions

2023-08-25T14:36:49.194373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-25T14:34:37.486448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-25T14:37:58.925444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-25T14:36:15.477414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-25T14:38:12.548012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idoverall_pickteamnationalitypositionage
id1.0000.0201.0001.0001.0001.000
overall_pick0.0201.0000.0250.0420.0000.131
team1.0000.0251.0000.0280.0500.078
nationality1.0000.0420.0281.0000.1410.100
position1.0000.0000.0500.1411.0000.039
age1.0000.1310.0780.1000.0391.000

Missing values

2023-08-25T14:38:01.151115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-25T14:38:01.468731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idyearoverall_pickteamplayernationalitypositionageto_yearamateur_teamgames_playedgoalsassistspointsplus_minuspenalties_minutesgoalie_games_playedgoalie_winsgoalie_lossesgoalie_ties_overtimesave_percentagegoals_against_averagepoint_shares
0120221Montreal CanadiensJuraj SlafkovskySKLW18TPS (Finland)
1220222New Jersey DevilsSimon NemecSKD18HK Nitra (Slovakia)
2320223Arizona CoyotesLogan CooleyUSC18USA U-18 Development Team (USDP/USHL)
3420224Seattle KrakenShane WrightCAC18Kingston Frontenacs (OHL)
4520225Philadelphia FlyersCutter GauthierSELW18USA U-18 Development Team (USDP/USHL)
5620226Columbus Blue JacketsDavid JiricekCZD18HC Plzen (Czech)
6720227Chicago BlackhawksKevin KorchinskiCAD18Seattle Thunderbirds (WHL)
7820228Detroit Red WingsMarco KasperATC18Rogle BK (Sweden)
8920229Buffalo SabresMatthew SavoieCAC18Winnipeg Ice (WHL)
910202210Anaheim DucksPavel MintyukovRUD18Saginaw Spirit (OHL)
idyearoverall_pickteamplayernationalitypositionageto_yearamateur_teamgames_playedgoalsassistspointsplus_minuspenalties_minutesgoalie_games_playedgoalie_winsgoalie_lossesgoalie_ties_overtimesave_percentagegoals_against_averagepoint_shares
1224012241196312Toronto Maple LeafsNeil ClairmontCALWParry Sound Midgets ()
1224112242196313Montreal CanadiensRoy PughCACAurora (OJHL)
1224212243196314Boston BruinsRoger BamburakCARWIsaac Brock (GWMHA)
1224312244196315New York RangersMike CumminsCAGeorgetown Midgets (OHA-Jr.)
1224412245196316Chicago BlackhawksBill CarsonCADBrampton Midgets ()
1224512246196317Toronto Maple LeafsJim McKennyCAD161979Toronto Neil McNeil Maroons (MetJAHL)60482247329-429453.7
1224612247196318Montreal CanadiensGlen ShirtonCADPort Colborne Midgets ()
1224712248196319Boston BruinsJim BlairCAFGeorgetown Midgets (OHA-Jr.)
1224812249196320New York RangersCampbell AllesonCADPortage la Prairie Jr. ()
1224912250196321Toronto Maple LeafsGerry MeehanCAC171979Toronto Neil McNeil Maroons (MetJAHL)670180243423-11311130.5